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Article: An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks

TitleAn approximation method for solving the steady-state probability distribution of probabilistic Boolean networks
Authors
Issue Date2007
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2007, v. 23 n. 12, p. 1511-1518 How to Cite?
AbstractMotivation: Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2 n-by- 2 n where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method. Results: In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method. © The Author 2007. Published by Oxford University Press. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/75171
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorZhang, Sen_HK
dc.contributor.authorNg, MKen_HK
dc.contributor.authorAkutsu, Ten_HK
dc.date.accessioned2010-09-06T07:08:37Z-
dc.date.available2010-09-06T07:08:37Z-
dc.date.issued2007en_HK
dc.identifier.citationBioinformatics, 2007, v. 23 n. 12, p. 1511-1518en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75171-
dc.description.abstractMotivation: Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2 n-by- 2 n where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method. Results: In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method. © The Author 2007. Published by Oxford University Press. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
dc.rightsBioinformatics. Copyright © Oxford University Press.en_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshComputational Biology - methodsen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshGene Expression Regulationen_HK
dc.subject.meshMarkov Chainsen_HK
dc.subject.meshModels, Geneticen_HK
dc.subject.meshModels, Statisticalen_HK
dc.subject.meshMonte Carlo Methoden_HK
dc.subject.meshProbabilityen_HK
dc.subject.meshStochastic Processesen_HK
dc.titleAn approximation method for solving the steady-state probability distribution of probabilistic Boolean networksen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=23&spage=1511&epage=1518&date=2007&atitle=An+Approximation+Method+for+Solving+the+Steady-state+Probability+Distribution+of+Probabilistic+Boolean+Networksen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1093/bioinformatics/btm142en_HK
dc.identifier.pmid17463027-
dc.identifier.scopuseid_2-s2.0-34547840185en_HK
dc.identifier.hkuros130680en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34547840185&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue12en_HK
dc.identifier.spage1511en_HK
dc.identifier.epage1518en_HK
dc.identifier.eissn1460-2059-
dc.identifier.isiWOS:000248271700010-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridZhang, S=10143093600en_HK
dc.identifier.scopusauthoridNg, MK=34571761900en_HK
dc.identifier.scopusauthoridAkutsu, T=7102080520en_HK
dc.identifier.citeulike1319068-

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